Files and links (1)
Accepted (AM)Open Access (License Unspecified), Open
Journal article
Effect of Pooled Comparative Information on Judgments of Quality
IEEE transactions on human-machine systems, v 45(6), pp 773-781
Dec 2015
PMID: 26949581
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Quality assessment is the focus of many healthcare initiatives. Yet, it is not well understood how the type of information used in decision support tools to enable judgments of quality based on data impacts the accuracy, consistency, and reliability of judgments made by physicians. Comparative pooled information could allow physicians to judge the quality of their practice by making comparisons with other practices or other specific populations of patients. In this study, resident physicians were provided with varying types of information derived from pooled patient datasets: quality component measures at the individual and group level, a qualitative interpretation of the quality measures using percentile rank, and an aggregate composite quality score. Thirty-two participants viewed 30 quality profiles consisting of information applicable to the practice of 30 deidentified resident physicians. Those provided with quality component measures and a qualitative interpretation of the quality measures (rankings) judged quality of care more similarly to experts and were more internally consistent compared with participants who were provided with quality component measures alone. Reliability between participants was significantly less for those who were provided with a composite quality score compared with those who were not.
Metrics
Details
- Title
- Effect of Pooled Comparative Information on Judgments of Quality
- Creators
- Leigh A Baumgart - NorthShore University HealthSystemEllen J Bass - Drexel University College of Nursing and Health ProfessionsJohn D Voss - University of VirginiaJason A Lyman - Department of Public Health Sciences , University of Virginia , Charlottesville, VA, USA
- Publication Details
- IEEE transactions on human-machine systems, v 45(6), pp 773-781
- Publisher
- IEEE
- Grant note
- T15LM009462 / National Library of Medicine (10.13039/100000092)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000366893700011
- Scopus ID
- 2-s2.0-84959506888
- Other Identifier
- 991019168711704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Cybernetics